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test_otu_significance.py
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test_otu_significance.py
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#!/usr/bin/env python
# File created on 16 Aug 2013
from __future__ import division
__author__ = "Luke Ursell"
__copyright__ = "Copyright 2013, The QIIME project"
__credits__ = ["Luke Ursell", "Will Van Treuren", "Jai Ram Rideout"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "Luke Ursell"
__email__ = "lkursell@gmail.com"
from unittest import TestCase, main
from qiime.otu_significance import (get_sample_cats, get_cat_sample_groups,
get_sample_indices, group_significance_row_generator, sort_by_pval,
run_group_significance_test, group_significance_output_formatter,
GROUP_TEST_CHOICES, grouped_correlation_row_generator,
run_grouped_correlation, CORRELATION_TEST_CHOICES,
grouped_correlation_formatter, correlation_row_generator,
run_correlation_test, is_computable_float,
correlate_output_formatter, _add_metadata)
from qiime.stats import (assign_correlation_pval, fisher,
fisher_population_correlation)
from numpy import array, hstack, corrcoef, asarray, nan, inf
from numpy.random import seed
from numpy.testing import assert_almost_equal
from os import remove
from qiime.parse import parse_mapping_file_to_dict, parse_otu_table
from biom.parse import parse_biom_table
class GroupSignificanceFunctionsTests(TestCase):
"""Tests of group significance functions."""
def setUp(self):
"""Define values used by all tests."""
# nothing to do, all tests use different things
pass
def test_get_sample_cats(self):
"""Test get_sample_cats."""
pmf_in = \
{'Sample1': {'test_cat': 'cat1', 'test_corr': '1', 'test_empty': 'abc'},
'Sample2':
{'test_cat': 'cat1', 'test_corr': '1', 'test_empty': 'abc'},
'Sample3':
{'test_cat': 'cat2', 'test_corr': '1', 'test_empty': ''},
'Sample4':
{'test_cat': 'cat2', 'test_corr': '1', 'test_empty': ''},
'Sample5':
{'test_cat': 'cat3', 'test_corr': '1', 'test_empty': 'abc'},
'Sample6': {'test_cat': 'cat3', 'test_corr': '1', 'test_empty': ''}}
# test with properly formatted, ordered parsed mapping file
category = 'test_cat'
exp = {'Sample1': 'cat1',
'Sample2': 'cat1',
'Sample3': 'cat2',
'Sample4': 'cat2',
'Sample5': 'cat3',
'Sample6': 'cat3'}
obs = get_sample_cats(pmf_in, category)
self.assertEqual(exp, obs)
# test with only one category
category = 'test_corr'
exp = {'Sample1': '1',
'Sample2': '1',
'Sample3': '1',
'Sample4': '1',
'Sample5': '1',
'Sample6': '1'}
obs = get_sample_cats(pmf_in, category)
self.assertEqual(exp, obs)
# test with empty categories
category = 'test_empty'
exp = {'Sample1': 'abc',
'Sample2': 'abc',
'Sample5': 'abc'}
obs = get_sample_cats(pmf_in, category)
self.assertEqual(exp, obs)
def test_get_cat_sample_groups(self):
"""Test get_cat_sample_groups works."""
sample_cats = {'Sample1': 'cat1',
'Sample2': 'cat1',
'Sample3': 'cat2',
'Sample4': 'cat2',
'Sample5': 'cat3',
'Sample6': 'cat3'}
exp = {'cat1': ['Sample1', 'Sample2'],
'cat2': ['Sample4', 'Sample3'],
'cat3': ['Sample5', 'Sample6']}
obs = get_cat_sample_groups(sample_cats)
self.assertEqual(exp, obs)
def test_get_sample_indices(self):
"""Test get_sample_indices works"""
cat_sample_groups = {'cat1': ['Sample1', 'Sample2'],
'cat2': ['Sample4', 'Sample3'],
'cat3': ['Sample5', 'Sample6']}
bt = parse_biom_table(BT_IN_1)
exp = {'cat1': [0, 1], 'cat2': [3, 2], 'cat3': [4, 5]}
obs = get_sample_indices(cat_sample_groups, bt)
self.assertEqual(exp, obs)
def test_group_significance_row_generator(self):
"""Test group_significance_row_generator works."""
# run with ordered example
sample_indices = {'cat1': [0, 1], 'cat2': [3, 2], 'cat3': [4, 5]}
bt = parse_biom_table(BT_IN_1)
data = asarray([d for d in bt.iter_data(dense=True,
axis='observation')])
obs = list(group_significance_row_generator(bt, sample_indices))
exp = zip(data.take([0, 1], 1),
data.take([3, 2], 1), data.take([4, 5], 1))
for o, e in zip(obs, exp):
assert_almost_equal(e, o)
# run with unequal length example
sample_indices = {'g0': [0, 1, 4, 5], 'g1': [3], 'g2': [2]}
obs = list(group_significance_row_generator(bt, sample_indices))
exp = zip(data.take([0, 1, 4, 5], 1),
data.take([3], 1), data.take([2], 1))
for o, e in zip(obs, exp):
[assert_almost_equal(i, j) for i, j in
zip(sorted(hstack(e)), sorted(hstack(o)))]
# the order of e and o are not the same. this isn't a problem in
# the actual function because we iterate over the order of the
# sample keys every time.
def test_run_group_significance_test(self):
"""Test that all group significance tests can be run."""
bt = parse_biom_table(BT_IN_1)
bt_2 = parse_biom_table(BT_IN_2)
bt_4 = parse_biom_table(BT_4)
# test with non-paramteric t-test
sample_indices = {'cat1': [0, 5, 1], 'cat2': [2, 4, 3]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.17503798979747345, 0.20029818620053824,
-1.5065313062753816, -
0.043884559904114794, -1.0631239617935129,
-1.2878361428003895]
# we are expecting 1001 comparisons)
exp_pvals = map(lambda x: x / 1001., [888, 899, 279, 1001, 489, 299])
exp_means = [[52.333333333333336, 48.333333333333336],
[34.0, 30.333333333333332],
[20.0, 49.333333333333336],
[55.333333333333336, 56.0],
[20.0, 38.0],
[30.0, 60.333333333333336]]
seed(0) # seed prng for reproducibility
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'nonparametric_t_test',
GROUP_TEST_CHOICES, reps=1000)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with nonparametric t-test but different ordering
sample_indices = {'cat1': [0, 1, 5], 'cat2': [4, 3, 2]}
row_gen = group_significance_row_generator(bt, sample_indices)
seed(0) # seed prng for reproducibility
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'nonparametric_t_test',
GROUP_TEST_CHOICES, reps=1000)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with BT_4 biom table
sample_indices = {'cat1': [0, 3, 1, 4], 'cat2': [5, 2, 7, 6]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [-0.38741397129147953, -0.38334158591463874,
0.077468274988510541, -
0.2322539745918096, 0.16469600468808282,
-0.49589486133213057]
# we are expecting 1001 comparisons)
exp_pvals = map(lambda x: x / 1001., [821, 719, 916, 935, 938, 604])
exp_means = [[43.5, 51.75],
[29.75, 34.75],
[41.5, 40.0],
[50.5, 53.75],
[28.0, 25.5],
[41.75, 54.0]]
seed(0)
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'nonparametric_t_test',
GROUP_TEST_CHOICES, reps=1000)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with parametric t test
# bt_1 agrees with Prism
sample_indices = {'cat1': [4, 1, 2], 'cat2': [5, 0, 3]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [-1.0504514628777806, -0.94113003446934629,
-
0.66264262463016887, 0.17617555832772411, 1.1144416530351877,
-1.2483315640812607]
exp_pvals = [0.3527834167236007, 0.39992473225679626,
0.5437923932346147, 0.8687158192049661, 0.32753202812350557,
0.27998887149482976]
exp_means = [[39.666666666666664, 61.0],
[24.333333333333332, 40.0],
[27.0, 42.333333333333336],
[57.0, 54.333333333333336],
[38.333333333333336, 19.666666666666668],
[30.333333333333332, 60.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'parametric_t_test',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with BT_4
sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [0.43577690622483684, -2.5911938781738648,
-
1.3573515147239095, 1.2101173913086851, 2.137178815882979,
0.0099191576638653078]
exp_pvals = [0.67823972846362579, 0.041145883121579255,
0.2235024418313547, 0.27174025956151748, 0.076447615888438444,
0.9924073718332862]
exp_means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'parametric_t_test',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with bootstrapped mann_whitney_u
sample_indices = {'cat1': [4, 1, 2], 'cat2': [5, 0, 3]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [2.0, 2.0, 2.0, 3.0, 2.0, 2.0]
exp_pvals = array([0.22977023, 0.2047952, 0.19280719, 0.44255744,
0.18781219, 0.22477522])
exp_means = [[39.666666666666664, 61.0],
[24.333333333333332, 40.0],
[27.0, 42.333333333333336],
[57.0, 54.333333333333336],
[38.333333333333336, 19.666666666666668],
[30.333333333333332, 60.0]]
seed(0) # seed prng for reproducibility
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'bootstrap_mann_whitney_u',
GROUP_TEST_CHOICES, reps=1000)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with BT_4
sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [6.0, 1.0, 5.0, 2.0, 1.0, 7.0]
exp_pvals = array([0.49050949, 0.01598402, 0.31968032, 0.06193806,
0.02997003, 0.6953047])
exp_means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
seed(0) # seed prng for reproducibility
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'bootstrap_mann_whitney_u',
GROUP_TEST_CHOICES, reps=1000)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with parametric mann whitney u
sample_indices = {'cat1': [0, 3, 1], 'cat2': [4, 2, 5]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [3.0, 3.0, 4.0, 4.0, 3.0, 4.0]
exp_pvals = array([0.66252058, 0.66252058, 1., 1., 0.66252058, 1.])
exp_means = [[52.666666666666664, 48.0],
[23.666666666666668, 40.666666666666664],
[34.0, 35.333333333333336],
[56.333333333333336, 55.0],
[32.333333333333336, 25.666666666666668],
[46.0, 44.333333333333336]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'mann_whitney_u',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with BT_4
sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [6.0, 1.0, 5.0, 2.0, 1.0, 7.0]
exp_pvals = array([0.66500554, 0.06060197, 0.46782508, 0.1123512,
0.06060197, 0.88523391])
exp_means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'mann_whitney_u',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with ANOVA
sample_indices = {'cat1': [0, 3], 'cat2': [4, 5], 'cat3': [2, 1]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.022340083574413375, 20.028268551236753,
2.086854460093897, 0.96500593119810185, 4.8390804597701154,
0.54346882684796749]
exp_pvals = [0.97806870848824634, 0.018391757629969238,
0.27043709109167957, 0.47468983920325486, 0.11510587547067222,
0.62890473306440042]
exp_means = [[53.0, 46.5, 51.5],
[28.5, 55.5, 12.5],
[50.0, 45.5, 8.5],
[50.5, 47.5, 69.0],
[28.0, 9.0, 50.0],
[65.0, 39.5, 31.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'ANOVA',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with uneven group sizes
sample_indices = {'cat1': [0, 2, 3, 1], 'cat2': [4, 5]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.05663963168179019, 16.436058700209646,
0.43828937472444823, 0.675244322576109, 4.7713717693836974,
0.083541102077687446]
exp_pvals = [0.8235822412182755, 0.015422975290359022,
0.54414414026513325, 0.45738578176242134, 0.094285405564661875,
0.78691584834507211]
exp_means = [[52.25, 46.5],
[20.5, 55.5],
[29.25, 45.5],
[59.75, 47.5],
[39.0, 9.0],
[48.0, 39.5]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'ANOVA',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with bt_4
sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [0.18990151199889027, 6.7142857142857144,
1.8424031345232912, 1.4643841007477372, 4.5675332910589734,
9.8389688760617899e-05]
exp_pvals = [0.6782397284636259, 0.041145883121579234,
0.22350244183135481, 0.27174025956151771, 0.076447615888438403,
0.9924073718332751]
exp_means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'ANOVA',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with g goodness of fit
sample_indices = {'cat1': [0, 3], 'cat2': [4, 5], 'cat3': [2, 1]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.46328913071721711,
29.810689447160001, 37.234612591840595, 4.7031232724401875,
31.207185565457102, 13.332324853339509]
exp_pvals = [0.79322801392154108,
3.3627225458535774e-07, 8.2149818410655555e-09, 0.09522034650579822,
1.6728066897036456e-07, 0.00127327567601971]
exp_means = [[53.0, 46.5, 51.5],
[28.5, 55.5, 12.5],
[50.0, 45.5, 8.5],
[50.5, 47.5, 69.0],
[28.0, 9.0, 50.0],
[65.0, 39.5, 31.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'g_test',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with individual groups
sample_indices = {'cat1': [0], 'cat2': [1], 'cat3': [3],
'cat4': [2], 'cat5': [5], 'cat6': [4]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [68.7536611489639, 62.908926545455522,
115.84654226008865, 26.819713749563704, 84.940231595557307,
105.37909384565077]
exp_pvals = [1.8616725644907271e-13, 3.0403858229558975e-12,
2.3772983815049693e-23, 6.1843461955812955e-05,
7.7481603433718027e-17, 3.8768150325829967e-21]
exp_means = [[28.0, 52.0, 78.0, 51.0, 77.0, 16.0],
[25.0, 14.0, 32.0, 11.0, 63.0, 48.0],
[31.0, 2.0, 69.0, 15.0, 27.0, 64.0],
[36.0, 68.0, 65.0, 70.0, 62.0, 33.0],
[16.0, 41.0, 40.0, 59.0, 3.0, 15.0],
[32.0, 8.0, 98.0, 54.0, 50.0, 29.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'g_test',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with uneven length groups
sample_indices = {'cat1': [0, 3, 4, 5], 'cat3': [2, 1]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.030099778845406742,
16.703388149486191, 29.941854048163027, 3.39187772427496,
14.935738277477988, 5.4519230964604013]
exp_pvals = [0.86226402523867973, 4.3702877865113464e-05,
4.451983032513133e-08, 0.065518295867083964, 0.00011123571448583719,
0.019546798231055287]
exp_means = [[49.75, 51.5],
[42.0, 12.5],
[47.75, 8.5],
[49.0, 69.0],
[18.5, 50.0],
[52.25, 31.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'g_test',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with bt_4
sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [0.8950130401309585, 8.6948783805472942,
6.5397009199496443, 2.2281537448054953, 11.541070115516771,
0.00064935138712822981]
exp_pvals = [0.34412242732851783, 0.0031910540870178925,
0.010549308294222293, 0.13551569348660794, 0.00068075444949030543,
0.97967020739471489]
exp_means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'g_test',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with Kruskal Wallis
sample_indices = {'cat1': [0, 3], 'cat2': [4, 5], 'cat3': [2, 1]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.2857142857142847,
4.5714285714285694, 3.7142857142857117, 3.7142857142857117,
4.5714285714285694, 0.85714285714285765]
exp_pvals = [0.86687789975018215, 0.10170139230422694,
0.15611804531597129, 0.15611804531597129, 0.10170139230422694,
0.65143905753105535]
exp_means = [[53.0, 46.5, 51.5],
[28.5, 55.5, 12.5],
[50.0, 45.5, 8.5],
[50.5, 47.5, 69.0],
[28.0, 9.0, 50.0],
[65.0, 39.5, 31.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'kruskal_wallis',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with individual groups
sample_indices = {'cat1': [0], 'cat2': [1], 'cat3': [3],
'cat4': [2], 'cat5': [5], 'cat6': [4]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [5.0, 5.0, 5.0, 5.0, 5.0, 5.0]
exp_pvals = [0.41588018699550794, 0.41588018699550794,
0.41588018699550794, 0.41588018699550794, 0.41588018699550794,
0.41588018699550794]
exp_means = [[28.0, 52.0, 78.0, 51.0, 77.0, 16.0],
[25.0, 14.0, 32.0, 11.0, 63.0, 48.0],
[31.0, 2.0, 69.0, 15.0, 27.0, 64.0],
[36.0, 68.0, 65.0, 70.0, 62.0, 33.0],
[16.0, 41.0, 40.0, 59.0, 3.0, 15.0],
[32.0, 8.0, 98.0, 54.0, 50.0, 29.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'kruskal_wallis',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with uneven length groups
sample_indices = {'cat1': [0, 3, 4, 5], 'cat3': [2, 1]}
row_gen = group_significance_row_generator(bt, sample_indices)
exp_test_stats = [0.0, 3.428571428571427, 3.428571428571427,
3.428571428571427, 3.428571428571427, 0.21428571428571175]
exp_pvals = [1, 0.064077506451059238, 0.064077506451059238,
0.064077506451059238, 0.064077506451059238, 0.64342884356362262]
exp_means = [[49.75, 51.5],
[42.0, 12.5],
[47.75, 8.5],
[49.0, 69.0],
[18.5, 50.0],
[52.25, 31.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'kruskal_wallis',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# test with bt_4
sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
row_gen = group_significance_row_generator(bt_4, sample_indices)
exp_test_stats = [0.33333333333333215, 4.0833333333333321,
0.75903614457831325, 3.0, 4.0833333333333321, 0.083333333333332149]
exp_pvals = [0.56370286165077377, 0.043308142810792101,
0.38363032713198986, 0.08326451666355042, 0.043308142810792101,
0.77282999268444919]
exp_means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'kruskal_wallis',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
# Test with equal data point, which should normally cause an error
# in kruskal wallis
sample_indices = {'cat1': [0, 3], 'cat2': [4, 5], 'cat3': [2, 1]}
row_gen = group_significance_row_generator(bt_2, sample_indices)
exp_test_stats = [nan, 4.5714285714285694, 3.7142857142857117,
3.7142857142857117, 4.5714285714285694,
0.85714285714285765]
exp_pvals = [nan, 0.10170139230422694, 0.15611804531597129,
0.15611804531597129, 0.10170139230422694,
0.65143905753105535]
exp_means = [[28.0, 28.0, 28.0],
[30.0, 55.5, 12.5],
[50.0, 45.5, 8.5],
[50.5, 47.5, 69.0],
[28.0, 9.0, 50.0],
[65.0, 39.5, 31.0]]
obs_test_stats, obs_pvals, obs_means = \
run_group_significance_test(row_gen, 'kruskal_wallis',
GROUP_TEST_CHOICES)
assert_almost_equal(exp_test_stats, obs_test_stats)
assert_almost_equal(exp_pvals, obs_pvals)
assert_almost_equal(exp_means, obs_means)
def test_group_significance_output_formatter(self):
"""output_formatter works"""
# Using ANOVA test for example
otu_table_1 = """{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BIOM-Format 1.1.2","date": "2013-08-16T15:23:02.872397","matrix_type": "sparse","matrix_element_type": "float","shape": [6, 8],"data": [[0,0,28.0],[0,1,52.0],[0,2,51.0],[0,3,78.0],[0,4,16.0],[0,5,77.0],[0,6,73.0],[0,7,6.0],[1,0,25.0],[1,1,14.0],[1,2,11.0],[1,3,32.0],[1,4,48.0],[1,5,63.0],[1,6,27.0],[1,7,38.0],[2,0,31.0],[2,1,2.0],[2,2,15.0],[2,3,69.0],[2,4,64.0],[2,5,27.0],[2,6,64.0],[2,7,54.0],[3,0,36.0],[3,1,68.0],[3,2,70.0],[3,3,65.0],[3,4,33.0],[3,5,62.0],[3,6,60.0],[3,7,23.0],[4,0,16.0],[4,1,41.0],[4,2,59.0],[4,3,40.0],[4,4,15.0],[4,5,3.0],[4,6,35.0],[4,7,5.0],[5,0,32.0],[5,1,8.0],[5,2,54.0],[5,3,98.0],[5,4,29.0],[5,5,50.0],[5,6,93.0],[5,7,19.0]],"rows": [{"id": "OTU1", "metadata": {"taxonomy": "k__One"}},{"id": "OTU2", "metadata": {"taxonomy": "k__Two"}},{"id": "OTU3", "metadata": {"taxonomy": "k__Three"}},{"id": "OTU4", "metadata": {"taxonomy": "k__Four"}},{"id": "OTU5", "metadata": {"taxonomy": "k__Five"}},{"id": "OTU6", "metadata": {"taxonomy": "k__Six"}}],"columns": [{"id": "Sample1", "metadata": null},{"id": "Sample2", "metadata": null},{"id": "Sample3", "metadata": null},{"id": "Sample4", "metadata": null},{"id": "Sample5", "metadata": null},{"id": "Sample6", "metadata": null},{"id": "Sample7", "metadata": null},{"id": "Sample8", "metadata": null}]}"""
bt = parse_biom_table(otu_table_1)
test_stats = [0.18990151199889027,
6.7142857142857144,
1.8424031345232912,
1.4643841007477372,
4.5675332910589734,
9.8389688760617899e-05]
pvals = [0.6782397284636259,
0.041145883121579234,
0.22350244183135481,
0.27174025956151771,
0.076447615888438403,
0.9924073718332751]
means = [[52.25, 43.0],
[20.5, 44.0],
[29.25, 52.25],
[59.75, 44.5],
[39.0, 14.5],
[48.0, 47.75]]
fdr_pvals = [0.8138876741563511,
0.24687529872947539,
0.44700488366270963,
0.40761038934227656,
0.22934284766531521,
0.9924073718332751]
bon_pvals = [4.069438370781755,
0.2468752987294754,
1.3410146509881289,
1.6304415573691062,
0.4586856953306304,
5.95444423099965]
cat_sample_indices = {'cat1': [0, 1, 2, 3], 'cat2': [4, 5, 6, 7]}
line_header_out = 'OTU\tTest-Statistic\tP\tFDR_P\tBonferroni_P\tcat1_mean\tcat2_mean\ttaxonomy'
line_1_out = 'OTU1\t0.189901511999\t0.678239728464\t0.813887674156\t4.06943837078\t52.25\t43.0\tk__One'
line_6_out = 'OTU6\t9.83896887606e-05\t0.992407371833\t0.992407371833\t5.954444231\t48.0\t47.75\tk__Six'
lines = group_significance_output_formatter(bt, test_stats, pvals,
fdr_pvals, bon_pvals, means, cat_sample_indices, md_key='taxonomy')
self.assertEqual(lines[0], line_header_out)
self.assertEqual(lines[1], line_1_out)
self.assertEqual(lines[6], line_6_out)
def test_sort_by_pval(self):
"""sort_by_pval works"""
lines = [
'OTU\tTest-Statistic\tP\tFDR_P\tBonferroni_P\tcat1_mean\tcat2_mean\tTaxonomy',
'OTU1\t0.189901511999\t0.678239728464\t0.813887674156\t4.06943837078\t52.25\t43.0\tk__One',
'OTU2\t6.71428571429\t0.0411458831216\t0.246875298729\t0.246875298729\t20.5\t44.0\tk__Two',
'OTU3\t1.84240313452\t0.223502441831\t0.447004883663\t1.34101465099\t29.25\t52.25\tk__Three',
'OTU4\t1.46438410075\t0.271740259562\t0.407610389342\t1.63044155737\t59.75\t44.5\tk__Four',
'OTU5\t4.56753329106\t0.0764476158884\t0.229342847665\t0.458685695331\t39.0\t14.5\tk__Five',
'OTU6\t9.83896887606e-05\t0.992407371833\t0.992407371833\t5.954444231\t48.0\t47.75\tk__Six']
lines_sorted_pval_1 = \
'OTU2\t6.71428571429\t0.0411458831216\t0.246875298729\t0.246875298729\t20.5\t44.0\tk__Two'
lines_sorted_fdr_1 = \
'OTU5\t4.56753329106\t0.0764476158884\t0.229342847665\t0.458685695331\t39.0\t14.5\tk__Five'
lines_sorted_bonf_6 = \
'OTU6\t9.83896887606e-05\t0.992407371833\t0.992407371833\t5.954444231\t48.0\t47.75\tk__Six'
lines_pval = sort_by_pval(lines, 2)
lines_pval_fdr = sort_by_pval(lines, 3)
lines_pval_bonf = sort_by_pval(lines, 4)
self.assertEqual(lines_pval[1], lines_sorted_pval_1)
self.assertEqual(lines_pval_fdr[1], lines_sorted_fdr_1)
self.assertEqual(lines_pval_bonf[6], lines_sorted_bonf_6)
class GroupedCorrelationTests(TestCase):
"""Tests of grouped correlation significance functions."""
def setUp(self):
"""Define values used by all tests."""
self.bt_str = '{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BIOM-Format 1.2.0-dev","date": "2013-11-28T16:50:27.438635","matrix_type": "sparse","matrix_element_type": "float","shape": [10, 10],"data": [[0,0,22.0],[0,4,15.0],[0,5,74.0],[0,6,34.0],[0,7,76.0],[0,9,48.0],[1,0,70.0],[1,2,30.0],[1,3,37.0],[1,4,24.0],[1,5,77.0],[1,6,71.0],[1,7,58.0],[1,8,43.0],[2,1,2.0],[2,2,90.0],[2,4,48.0],[2,5,54.0],[2,7,22.0],[2,8,91.0],[3,0,80.0],[3,1,86.0],[3,3,78.0],[3,7,12.0],[4,4,68.0],[4,7,76.0],[4,8,57.0],[5,2,23.0],[5,5,66.0],[5,7,51.0],[5,9,77.0],[6,0,31.0],[6,1,47.0],[6,2,16.0],[6,4,96.0],[6,5,9.0],[7,0,17.0],[7,2,52.0],[7,5,11.0],[7,7,22.0],[8,1,74.0],[8,2,7.0],[8,4,80.0],[8,7,59.0],[9,0,6.0],[9,2,34.0],[9,3,63.0],[9,4,77.0],[9,5,8.0],[9,6,38.0],[9,7,73.0],[9,8,98.0],[9,9,45.0]],"rows": [{"id": "o1 ", "metadata": {"taxonomy": ["bug1"]}},{"id": "o2", "metadata": {"taxonomy": ["bug2"]}},{"id": "o3", "metadata": {"taxonomy": ["bug3"]}},{"id": "o4", "metadata": {"taxonomy": ["bug4"]}},{"id": "o5", "metadata": {"taxonomy": ["bug5"]}},{"id": "o6", "metadata": {"taxonomy": ["bug6"]}},{"id": "o7", "metadata": {"taxonomy": ["bug7"]}},{"id": "o8", "metadata": {"taxonomy": ["bug8"]}},{"id": "o9", "metadata": {"taxonomy": ["bug9"]}},{"id": "o10", "metadata": {"taxonomy": ["bug10"]}}],"columns": [{"id": "s1", "metadata": null},{"id": "s2", "metadata": null},{"id": "s6", "metadata": null},{"id": "s4", "metadata": null},{"id": "s5", "metadata": null},{"id": "s10", "metadata": null},{"id": "s7", "metadata": null},{"id": "s8", "metadata": null},{"id": "s9", "metadata": null},{"id": "s3", "metadata": null}]}'
self.mf_ordered = ['#SampleIDt\thsid\tfield\tval',
's1\t1\tf1\t6.1',
's3\t1\tf1\t0.0',
's7\t1\tf1\t14.2',
's9\t1\tf2\t6.5',
's2\t1\tf2\t21',
's6\t2\tf3\t0.3',
's5\t2\tf2\t9.1',
's4\t2\tf3\t0.8',
's8\t2\tf2\t5.0',
's10\t2\tf2\t11.']
self.mf_non_ordered = ['#SampleIDt\thsid\tval',
'Sample9\t1\t6.5',
'Sample8\t2\t5.0',
'Sample1\t1\t6.1',
'Sample2\t1\t21',
'Sample6\t2\t0.3',
'Sample5\t2\t9.1',
'Sample7\t1\t14.2',
'Sample4\t2\t0.8',
'Sample10\t2\t11.',
'Sample3\t1\t0.0']
self.cvs1 = ['1', '2']
self.mds1 = [[6.1, 0.0, 14.2, 6.5, 21], [.3, 9.1, .8, 5.0, 11.]]
self.otus1 = [
array([[22., 48., 34., 0., 0.],
[70., 0., 71., 43., 0.],
[0., 0., 0., 91., 2.],
[80., 0., 0., 0., 86.],
[0., 0., 0., 57., 0.],
[0., 77., 0., 0., 0.],
[31., 0., 0., 0., 47.],
[17., 0., 0., 0., 0.],
[0., 0., 0., 0., 74.],
[6., 45., 38., 98., 0.]]),
array([[0., 15., 0., 76., 74.],
[30., 24., 37., 58., 77.],
[90., 48., 0., 22., 54.],
[0., 0., 78., 12., 0.],
[0., 68., 0., 76., 0.],
[23., 0., 0., 51., 66.],
[16., 96., 0., 0., 9.],
[52., 0., 0., 22., 11.],
[7., 80., 0., 59., 0.],
[34., 77., 63., 73., 8.]])]
def test_grouped_correlation_row_generator(self):
"""Test that group row generator behaves as expected."""
category = 'val'
gc_to_samples = \
{'1': ['s1', 's3', 's7', 's9', 's2'],
'2': ['s6', 's5', 's4', 's8', 's10']}
bt = parse_biom_table(self.bt_str)
pmf = parse_mapping_file_to_dict(self.mf_ordered)[0]
obs_cvs, obs_mds, obs_otus = grouped_correlation_row_generator(bt, pmf,
category, gc_to_samples)
self.assertEqual(obs_cvs, self.cvs1)
assert_almost_equal(obs_mds, self.mds1)
assert_almost_equal(obs_otus, self.otus1)
# make sure it throws an error on non float md
self.assertRaises(ValueError, grouped_correlation_row_generator, bt,
pmf, 'field', gc_to_samples)
def test_run_grouped_correlation(self):
"""Test that grouped correlation values are calculated as expected."""
# hand calculation of spearman and pearson for 01
# md_g1 = array([6.1, 0.0, 14.2, 6.5, 21])
# md_g2 = array([.3, 9.1, .8, 5.0, 11])
# o1_g1 = array([22, 48, 34, 0, 0])
# o1_g2 = array([0, 15, 0, 76, 74])
# c1_g1 = -0.6155870112510925 #spearman(md_g1, o1_g1)
# c2_g2 = 0.66688592885535025 #spearman(md_g2, o1_g2)
# fisher_population_correlation([-0.6155870112510925,
# 0.66688592885535025], [5,5])
# fpc, h = (0.043595171909468329, 0.12776325359984511)
g1_rhos = [corrcoef(self.otus1[0][i], self.mds1[0])[0][1]
for i in range(10)]
g2_rhos = [corrcoef(self.otus1[1][i], self.mds1[1])[0][1]
for i in range(10)]
exp_rhos = [g1_rhos, g2_rhos]
g1_pvals = [assign_correlation_pval(g1_rhos[i], 5,
'parametric_t_distribution') for i in range(10)]
g2_pvals = [assign_correlation_pval(g2_rhos[i], 5,
'parametric_t_distribution') for i in range(10)]
exp_pvals = [g1_pvals, g2_pvals]
exp_f_pvals = [fisher([g1_pvals[i], g2_pvals[i]]) for i in range(10)]
tmp = [fisher_population_correlation([g1_rhos[i], g2_rhos[i]], [5, 5])
for i in range(10)]
exp_f_rhos = [x[0] for x in tmp]
exp_f_hs = [x[1] for x in tmp]
obs_rhos, obs_pvals, obs_f_pvals, obs_f_rhos, obs_f_hs = \
run_grouped_correlation(self.mds1, self.otus1, 'pearson',
CORRELATION_TEST_CHOICES, 'parametric_t_distribution')
assert_almost_equal(obs_rhos, exp_rhos)
assert_almost_equal(obs_pvals, exp_pvals)
assert_almost_equal(obs_f_pvals, exp_f_pvals)
assert_almost_equal(obs_f_rhos, exp_f_rhos)
assert_almost_equal(obs_f_hs, exp_f_hs)
def test_grouped_correlation_formatter(self):
"""Test that grouped correlation results are correctly formatted."""
obs_rhos, obs_pvals, obs_f_pvals, obs_f_rhos, obs_f_hs = \
run_grouped_correlation(self.mds1, self.otus1, 'pearson',
CORRELATION_TEST_CHOICES, 'parametric_t_distribution')
obs_lines = grouped_correlation_formatter(
parse_biom_table(self.bt_str),
obs_rhos, obs_pvals, obs_f_rhos, obs_f_pvals, obs_f_hs,
grouping_category='val', category_values=['1', '2'],
md_key='taxonomy')
exp_lines = [
'OTU\tRho_val:1\tRho_val:2\tPval_val:1\tPval_val:2\tFisher population correlation\tFisher combined p\tHomogeneity pval\ttaxonomy',
'o1 \t-0.549008525652\t0.624559974829\t0.337884839311\t0.26003464759\t0.0576788156554\t0.301540747632\t0.177206041905\tbug1',
'o2\t-0.0384383440919\t0.498044011456\t0.951070834767\t0.393160408737\t0.248789595012\t0.741753460366\t0.558440916618\tbug2',
'o3\t-0.193866521776\t0.0709448388675\t0.754716520102\t0.909746057157\t-0.0625618635573\t0.944764075331\t0.789149030542\tbug3',
'o4\t0.477058782953\t-0.535587095672\t0.416488072837\t0.352242311475\t-0.039368393563\t0.428279994782\t0.263944507542\tbug4',
'o5\t-0.210109340238\t0.321581922552\t0.734462039847\t0.5977201336\t0.0599902605341\t0.80041158791\t0.584587444442\tbug5',
'o6\t-0.656420030285\t0.443371522913\t0.228885504776\t0.454564967068\t-0.153808372937\t0.339487962449\t0.206619183728\tbug6',
'o7\t0.598119568608\t0.439213235895\t0.286674057126\t0.45931604814\t0.523199343913\t0.398633295268\t0.826682300837\tbug7',
'o8\t-0.237574613471\t-0.479408280331\t0.700380699877\t0.413860864444\t-0.364624217025\t0.648810427511\t0.779467050573\tbug8',
'o9\t0.785506814483\t0.359680356852\t0.115335393753\t0.552116179836\t0.615702620054\t0.239043351127\t0.494561919352\tbug9',
'o10\t-0.451199640732\t-0.216326825605\t0.445650543742\t0.72672774463\t-0.339035584667\t0.689001456839\t0.789926403391\tbug10']
self.assertEqual(obs_lines, exp_lines)
self.assertEqual(obs_lines, exp_lines)
def test_correlation_row_generator(self):
"""Test that correlation_row_generator works"""
otu_table_1 = """{"id": "None","format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org","type": "OTU table","generated_by":
"BIOM-Format 1.1.2","date": "2013-08-16T10:16:20.131837","matrix_type":
"sparse","matrix_element_type": "float","shape": [6, 6],"data": [[0,0,28.0],
[0,1,52.0],[0,2,51.0],[0,3,78.0],[0,4,16.0],[0,5,77.0],[1,0,25.0],[1,1,14.0],
[1,2,11.0],[1,3,32.0],[1,4,48.0],[1,5,63.0],[2,0,31.0],[2,1,2.0],[2,2,15.0],
[2,3,69.0],[2,4,64.0],[2,5,27.0],[3,0,36.0],[3,1,68.0],[3,2,70.0],[3,3,65.0],
[3,4,33.0],[3,5,62.0],[4,0,16.0],[4,1,41.0],[4,2,59.0],[4,3,40.0],[4,4,15.0],
[4,5,3.0],[5,0,32.0],[5,1,8.0],[5,2,54.0],[5,3,98.0],[5,4,29.0],[5,5,50.0]],
"rows": [{"id": "OTU1", "metadata": {"taxonomy": ["k__One"]}},{"id": "OTU2",
"metadata": {"taxonomy": ["k__Two"]}},{"id": "OTU3", "metadata": {"taxonomy":
["k__Three"]}},{"id": "OTU4", "metadata": {"taxonomy": ["k__Four"]}},{"id":
"OTU5", "metadata": {"taxonomy": ["k__Five"]}},{"id": "OTU6", "metadata":
{"taxonomy": ["k__Six"]}}],"columns": [{"id": "Sample1", "metadata": null},
{"id": "Sample2", "metadata": null},{"id": "Sample3", "metadata": null},
{"id": "Sample4", "metadata": null},{"id": "Sample5", "metadata": null},
{"id": "Sample6", "metadata": null}]}"""
bt = parse_biom_table(otu_table_1)
pmf = {'Sample1': {'test_cat': 'cat1', 'test_corr': '1'},
'Sample2': {'test_cat': 'cat1', 'test_corr': '2'},
'Sample3': {'test_cat': 'cat2', 'test_corr': '3'},
'Sample4': {'test_cat': 'cat2', 'test_corr': '4'},
'Sample5': {'test_cat': 'cat3', 'test_corr': '5'},
'Sample6': {'test_cat': 'cat3', 'test_corr': '6'}}
data_result = []
corr_row_gen_data = correlation_row_generator(bt, pmf, 'test_corr')
for i in corr_row_gen_data:
data_result.append(i)
data_output = [(array([28., 52., 51., 78., 16., 77.]),
array([1., 2., 3., 4., 5., 6.])),
(array([25., 14., 11., 32., 48., 63.]),
array([1., 2., 3., 4., 5., 6.])),
(array([31., 2., 15., 69., 64., 27.]),
array([1., 2., 3., 4., 5., 6.])),
(array([36., 68., 70., 65., 33., 62.]),
array([1., 2., 3., 4., 5., 6.])),
(array([16., 41., 59., 40., 15., 3.]),
array([1., 2., 3., 4., 5., 6.])),
(array([32., 8., 54., 98., 29., 50.]),
array([1., 2., 3., 4., 5., 6.]))]
assert_almost_equal(data_result, data_output)
def test_run_correlation_test(self):
"""Test run_correlation_test works."""
otu_table_1 = """{"id": "None","format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org","type": "OTU table","generated_by":
"BIOM-Format 1.1.2","date": "2013-08-16T10:16:20.131837","matrix_type":
"sparse","matrix_element_type": "float","shape": [6, 6],"data": [[0,0,28.0],
[0,1,52.0],[0,2,51.0],[0,3,78.0],[0,4,16.0],[0,5,77.0],[1,0,25.0],[1,1,14.0],
[1,2,11.0],[1,3,32.0],[1,4,48.0],[1,5,63.0],[2,0,31.0],[2,1,2.0],[2,2,15.0],
[2,3,69.0],[2,4,64.0],[2,5,27.0],[3,0,36.0],[3,1,68.0],[3,2,70.0],[3,3,65.0],
[3,4,33.0],[3,5,62.0],[4,0,16.0],[4,1,41.0],[4,2,59.0],[4,3,40.0],[4,4,15.0],
[4,5,3.0],[5,0,32.0],[5,1,8.0],[5,2,54.0],[5,3,98.0],[5,4,29.0],[5,5,50.0]],
"rows": [{"id": "OTU1", "metadata": {"taxonomy": ["k__One"]}},{"id": "OTU2",
"metadata": {"taxonomy": ["k__Two"]}},{"id": "OTU3", "metadata": {"taxonomy":
["k__Three"]}},{"id": "OTU4", "metadata": {"taxonomy": ["k__Four"]}},{"id":
"OTU5", "metadata": {"taxonomy": ["k__Five"]}},{"id": "OTU6", "metadata":
{"taxonomy": ["k__Six"]}}],"columns": [{"id": "Sample1", "metadata": null},
{"id": "Sample2", "metadata": null},{"id": "Sample3", "metadata": null},
{"id": "Sample4", "metadata": null},{"id": "Sample5", "metadata": null},
{"id": "Sample6", "metadata": null}]}"""
bt = parse_biom_table(otu_table_1)
pmf = {'Sample1': {'test_cat': 'cat1', 'test_corr': '1'},
'Sample2': {'test_cat': 'cat1', 'test_corr': '2'},
'Sample3': {'test_cat': 'cat2', 'test_corr': '3'},
'Sample4': {'test_cat': 'cat2', 'test_corr': '4'},
'Sample5': {'test_cat': 'cat3', 'test_corr': '5'},
'Sample6': {'test_cat': 'cat3', 'test_corr': '6'}}
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
# pearson
exp_ccs = [0.34884669332532803,
0.83015306552396662,
0.44037594794853846,
0.064224958686639605,
-0.41225244540969846,
0.34313146667442163]
exp_pvals = [0.49795623745457107,
0.04082210114420709,
0.38213734667034305,
0.90379502098011888,
0.41665291191825937,
0.50550281276602604]
exp_bootstrapped_pvals = [0.48599999999999999,
0.045999999999999999,
0.38200000000000001,
0.88200000000000001,
0.438,
0.54600000000000004]
obs_ccs, obs_pvals = run_correlation_test(data_gen, 'pearson',
CORRELATION_TEST_CHOICES,
pval_assignment_method='parametric_t_distribution')
assert_almost_equal(exp_ccs, obs_ccs)
assert_almost_equal(exp_pvals, obs_pvals)
# test with bootstrapped pvalues
seed(0)
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
obs_ccs, obs_pvals = run_correlation_test(data_gen, 'pearson',
CORRELATION_TEST_CHOICES, pval_assignment_method='bootstrapped',
permutations=1000)
# We can use the default decimal places in assert_almost_equal to test
# all p-values but the last one. This last one needs a different
# number of decimal places because the 479th permuted correlation
# coefficient can differ in equality to the observed correlation
# coefficient depending on the
# platform/numpy installation/configuration. Thus, the count of
# more-extreme correlation coefficients can differ by 1. With 1000
# permutations used in the test, this difference is 1 / 1000 = 0.001,
# hence the smaller number of decimal places used here in the
# comparison.
assert_almost_equal(obs_pvals[0], exp_bootstrapped_pvals[0])
assert_almost_equal(obs_pvals[1], exp_bootstrapped_pvals[1])
assert_almost_equal(obs_pvals[2], exp_bootstrapped_pvals[2], decimal=1)
assert_almost_equal(obs_pvals[3], exp_bootstrapped_pvals[3])
assert_almost_equal(obs_pvals[4], exp_bootstrapped_pvals[4])
assert_almost_equal(obs_pvals[5], exp_bootstrapped_pvals[5], decimal=2)
# spearman
exp_ccs = [0.25714285714285712,
0.77142857142857146,
0.31428571428571428,
-0.25714285714285712,
-0.60000000000000009,
0.25714285714285712]
exp_pvals = [0.62278717201166178,
0.07239650145772597,
0.54409329446064136,
0.62278717201166178,
0.20799999999999996,
0.62278717201166178]
exp_bootstrapped_pvals = [0.66200000000000003,
0.105,
0.56299999999999994,
0.65100000000000002,
0.26300000000000001,
0.65500000000000003]
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
obs_ccs, obs_pvals = run_correlation_test(data_gen, 'spearman',
CORRELATION_TEST_CHOICES,
pval_assignment_method='parametric_t_distribution')
assert_almost_equal(exp_ccs, obs_ccs)
assert_almost_equal(exp_pvals, obs_pvals)
# test with bootstrapped pvalues
seed(0)
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
obs_ccs, obs_pvals = run_correlation_test(data_gen, 'spearman',
CORRELATION_TEST_CHOICES, pval_assignment_method='bootstrapped',
permutations=1000)
assert_almost_equal(exp_bootstrapped_pvals, obs_pvals)
# kendall
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
exp_ccs = [0.2, 0.6, 0.2, -0.2, -0.4666666666666667, 0.2]
exp_pvals = [0.57302511935539036,
0.090873939986249083,
0.57302511935539036,
0.57302511935539036,
0.18848603806737496,
0.57302511935539036]
exp_bootstrapped_pvals = [0.73199999999999998,
0.14000000000000001,
0.72599999999999998,
0.70499999999999996,
0.29899999999999999,
0.71399999999999997]
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
obs_ccs, obs_pvals = run_correlation_test(data_gen, 'kendall',
CORRELATION_TEST_CHOICES,
pval_assignment_method='kendall')
assert_almost_equal(exp_ccs, obs_ccs)
assert_almost_equal(exp_pvals, obs_pvals)
# test with bootstrapped pvalues
seed(0)
data_gen = correlation_row_generator(bt, pmf, 'test_corr')
obs_ccs, obs_pvals = run_correlation_test(data_gen, 'kendall',
CORRELATION_TEST_CHOICES, pval_assignment_method='bootstrapped',
permutations=1000)
assert_almost_equal(exp_bootstrapped_pvals, obs_pvals)
def test_is_computable_float(self):
'''Test that an arbitrary input can be converted to float.'''
self.assertRaises(ValueError, is_computable_float, 'adkfjsdkfj')
assert is_computable_float(nan) == False
assert is_computable_float(inf) == False
assert is_computable_float(-inf) == False
assert is_computable_float("3.5679") == 3.5679
def test_correlate_output_formatter(self):
'''Test that output lines are correctly generated.'''
# use BT_IN_1
bt = parse_biom_table(BT_IN_1)
test_stats = [3.56, 2.78, nan, nan, nan, -4.78]
pvals = [.001, .034, nan, nan, nan, .00001]
fdr_pvals = [.001, .001, nan, nan, nan, .001]
bon_pvals = [.006, .192, nan, nan, nan, .006]
md_key = 'taxonomy'
type_of_stat = 'T'
exp = [['Feature ID', 'Test stat.', 'pval', 'pval_fdr', 'pval_bon',
md_key],
['OTU1', '3.56', '0.001', '0.001', '0.006', 'k__One'],
['OTU2', '2.78', '0.034', '0.001', '0.192', 'k__Two'],
['OTU3', 'nan', 'nan', 'nan', 'nan', 'k__Three'],
['OTU4', 'nan', 'nan', 'nan', 'nan', 'k__Four'],
['OTU5', 'nan', 'nan', 'nan', 'nan', 'k__Five'],
['OTU6', '-4.78', '1e-05', '0.001', '0.006', 'k__Six']]
exp = ['\t'.join(i) for i in exp]
obs = correlate_output_formatter(bt, test_stats, pvals, fdr_pvals,
bon_pvals, md_key)
assert obs == exp